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Summary
Summary
With low computational complexity and relatively short development time, Fuzzy Logic is an indispensable tool for engineering applications. The field is growing at an unprecedented rate, and there is a need for a book that describes essential tools, applications, examples, and perspectives in the field of fuzzy learning. The editors of Fuzzy Learning and Applications fill this need, providing an essential book for researchers, scientists, and engineers alike.
Organized into four parts, this book starts with the simplest learning method and gradually arrives at the most complex. First, it summarizes all the symbols and formulae used in the succeeding chapters and presents a historical overview of fuzzy learning. Next, it deals with current techniques, ranging from deterministic to hybrid methods. It then illustrates the enormous number of possibilities offered by fuzzy learning. Finally, it covers hardware dedicated to fuzzy learning, from digital to analog designs and implementations. With Fuzzy Learning and Applications, readers will discover the enormous possibilities fuzzy learning offers.
Author Notes
Russo, Marco; Jain, Lakhmi C.
Reviews 1
Choice Review
Each of the 11 chapters of this book is a report by the author or authors of the chapter describing advanced research in the area of fuzzy learning and its applications. The list of chapter headings indicates the scope of the research: "Evolutionary Fuzzy Learning" (describing the fusion of neural networks and genetic algorithms for fuzzy learning), "Fuzzy Controller Chip with Supervised Learning Capabilities," "Fuzzy Modeling in a Multi-Agent Framework," "Learning Techniques for Supervised Fuzzy Classifiers," "Multistage Fuzzy Control," "Learning Fuzzy Systems," "Use of Fuzzy Modeling in the Analysis of Rowing Speed," "Hopfield Coefficient Determination," "Fuzzy Control of a CD Player Focusing System," "Neuro-Fuzzy Scheduler for a Multimedia Web Server," and "A Neuro-Fuzzy System Based on Logical Interpretation of Fuzzy If_Then Rules." This book would be an excellent resource for graduate students and advanced researchers in two ways: it would enable them to learn about some of the advanced research and applications being carried out, and would provide them with a variety of ideas for further research and applications. R. Bharath emeritus, Northern Michigan University
Table of Contents
| 1 Evolutionary Fuzzy LearningMarco Russo | |
| Abstract | p. 2 |
| 1.1 Introduction | p. 2 |
| 1.2 Fuzzy knowledge representation | p. 2 |
| 1.3 Gefrex | p. 5 |
| General description | |
| The evolution algorithm | |
| Genetic coding | |
| Crossover | |
| The error | |
| Output singletons | |
| Hill climbing operator | |
| The fitness function | |
| Gefrex utilization | |
| Comparisons | |
| Computational complexity evaluation | |
| A learning example with compressed learning patterns | |
| 1.4 Pargefrex | p. 33 |
| A brief review of Gefrex | |
| The commodity supercomputer used | |
| Pargefrex description | |
| Performance evaluation | |
| 1.5 References | p. 48 |
| 2 A Stored-Programmable Mixed-Signal Fuzzy Controller Chip with Supervised Learning CapabilitiesFernando Vidal-Verdu and Rafael Navas and Manuel Delgado-Restituto and Angel Rodriguez-Vazquez | |
| Abstract | p. 52 |
| 2.1 Introduction | p. 52 |
| 2.2 Architecture and Functional Description | p. 54 |
| Inference Procedure | |
| Non-multiplexed Architecture | |
| Multiplexed Architecture | |
| 2.3 Analog Core Implementation | p. 60 |
| Non-Multiplexed Building Blocks | |
| Modifications for the Multiplexed Architecture | |
| 2.4 A/D Converters, Interval Selector and Digital Memory | p. 75 |
| A/D Converters | |
| Interval Selector | |
| Digital Memory | |
| 2.5 Learning Capability | p. 80 |
| Program Approach | |
| Learning Approach | |
| 2.6 Results and Conclusions | p. 85 |
| 2.7 Acknowledgement | p. 89 |
| 2.8 References | p. 89 |
| 3 Fuzzy Modeling in a Multi-Agent Framework for Learning in Autonomous SystemsJuan A. Botia and Humberto Martinez Barbera and Antonio F. Gomez Skarmeta | |
| Abstract | p. 94 |
| 3.1 Introduction | p. 94 |
| 3.2 Fuzzy Modeling and Agents | p. 95 |
| Distributed Artificial Intelligence(DAI) | |
| Tasks in MAS Development | |
| An Agentoriented Methodology | |
| MAST. The Multi-agent System Toolbox | |
| The MIX Agent model | |
| MAST: the Tool for Developing Multi-Agent Systems | |
| Exchanging Information Objects with MAST | |
| 3.3 A MAS Architecture for Fuzzy Modeling (MASAM) | p. 101 |
| Agents Architecture | |
| Fuzzy Modeling in our MAS Architecture | |
| Fuzzy Clustering: a Central Component for Fuzzy Modeling | |
| 3.4 Clustering Agents | p. 109 |
| Fuzzy LVQ | |
| Fuzzy Tabu Clustering | |
| Rule Generation Agents | |
| A Genetic Algorithm Based Method to Generate and/or Tune Fuzzy Rules | |
| 3.5 Robotics Application Example | p. 117 |
| Introduction | |
| The BG programming language | |
| Robot agents architecture | |
| Learning behaviour fusion | |
| Experimental results | |
| 3.6 Conclusions and Future Work | p. 133 |
| 3.7 References | p. 141 |
| 4 Learning Techniques for Supervised Fuzzy ClassifiersFrancesco Masulli and Alessandro Sperduti | |
| Abstract | p. 148 |
| 4.1 Introduction | p. 148 |
| 4.2 The Fuzzy Basis Function Network | p. 149 |
| 4.3 Bayes Optimal Classifier Approximation | p. 152 |
| 4.4 Learning in a FBFN Classifier | p. 154 |
| 4.5 Data Base and Preprocessing | p. 155 |
| 4.6 Classification Performances | p. 156 |
| 4.7 FBFN Structure Identification and Semantic Phase Transition | p. 158 |
| 4.8 The Simplified FBF Network and Its Extension | p. 159 |
| 4.9 Performance of the SFBF and ESFBF networks | p. 160 |
| 4.10 Hybrid Network | p. 162 |
| 4.11 Conclusions | p. 165 |
| 4.12 Acknowledgments | p. 166 |
| 4.13 References | p. 167 |
| 5 Multistage Fuzzy ControlZong-Mu Yeh and Hung-Pin Chen | |
| Abstract | p. 172 |
| 5.1 Introduction | p. 172 |
| Multistage fuzzy systems | |
| Related studies and problems | |
| Multistage approach | |
| 5.2 Multistage inference fuzzy systems | p. 179 |
| Multistage fuzzy inference engine | |
| Multistage fuzzy inference procedure | |
| 5.3 Methodology of fuzzy rule generation | p. 183 |
| Fuzzy rule generation for multi-stage fuzzy inference systems | |
| Fast multi-stage fuzzy logic inference | |
| 5.4 An illustrative example | p. 191 |
| 5.5 Conclusion | p. 199 |
| 5.6 References | p. 202 |
| 6 Learning Fuzzy SystemsAhmad Lotfi | |
| Abstract | p. 206 |
| 6.1 Introduction | p. 206 |
| 6.2 Fuzzy Systems | p. 207 |
| Example 1 Fuzzy Systems | |
| 6.3 Learning Fuzzy Systems | p. 211 |
| History | |
| Neural-fuzzy Systems | |
| Example 2 Neuro-fuzzy Systems | |
| Parameter Adjustment | |
| 6.4 Learning Rule | p. 214 |
| Example 3 Learning Fuzzy Systems | |
| 6.5 Interpretation Preservation | p. 216 |
| Constraint Learning | |
| Constraint Learning Rule | |
| Example 4 Interpretation Preservation of Learning Fuzzy Systems | |
| 6.6 Conclusions | p. 220 |
| 6.7 References | p. 221 |
| 7 An Application of Fuzzy Modeling to Analysis of Rowing Boat SpeedKanta Tachibana and Takeshi Furuhashi and Manabu Shimoda and Yasuo Kawakami and Tetsuo Fukunaga | |
| Abstract | p. 224 |
| 7.1 Introduction | p. 224 |
| 7.2 Complexities in rowing | p. 225 |
| 7.3 Fuzzy modeling | p. 226 |
| Fuzzy neural network | |
| Uneven division of input space | |
| 7.4 Experiments | p. 231 |
| 7.5 Modeling results | p. 235 |
| 7.6 Conclusion | p. 236 |
| 7.7 References | p. 240 |
| 8 A Novel Fuzzy Approach to Hopfield Coefficients DeterminationSalvatore Cavalieri and Marco Russo | |
| Abstract | p. 242 |
| 8.1 Introduction | p. 242 |
| 8.2 Hopfield-Type Neural Network | p. 244 |
| 8.3 Fuzzy Logic | p. 245 |
| 8.4 The Fuzzy Tuning of Hopfield Coefficients | p. 248 |
| A Detailed Description of the Algorithm for Coefficient Determination | |
| Membership Function Tuning | |
| 8.5 Examples of Application of the Proposed Method | p. 258 |
| The Traveling Salesman Problem | |
| Flexible Manufacturing System Performance Optimization | |
| 8.6 Remarks on the Tuning of the Parameters | p. 267 |
| 8.7 Description of the Fuzzy Inferences trained | p. 268 |
| 8.8 Fuzzy Approach versus Heuristic Determination of HCs | p. 270 |
| TSP by Hopfield and Tank's Original Energy Function | |
| TSP by Szu's Energy Function | |
| FMS Performance Optimization | |
| 8.9 Conclusions | p. 275 |
| 8.10 References | p. 276 |
| 9 Fuzzy control of a CD player focusing systemL.Fortuna and G.Muscato and R.Caponetto and M.G.Xibilia | |
| Abstract | p. 280 |
| 9.1 Introduction | p. 280 |
| 9.2 The CD player | p. 281 |
| 9.3 System identification | p. 284 |
| 9.4 Traditional controller synthesis | p. 285 |
| 9.5 Optimized fuzzy controller: the direct method | p. 287 |
| 9.6 The indirect optimization strategy | p. 290 |
| Approximation of the classical controller by a set of fuzzy rules | |
| Optimization of the fuzzy controller | |
| 9.7 Improvements introduced by the fuzzy controller | p. 295 |
| 9.8 Implementation details | p. 299 |
| 9.9 Conclusion | p. 301 |
| 9.10 References | p. 302 |
| 10 A Neuro-Fuzzy Scheduler for a Multimedia Web ServerZafar Ali and Arif Ghafoor and C.S.G.Lee | |
| Abstract | p. 306 |
| 10.1 Introduction | p. 306 |
| 10.2 Quality and Synchronization Requirement of Multimedia Information | p. 311 |
| Synchronization Requirements | |
| QOP Requirements | |
| 10.3 Synchronization in a Multimedia Web Environment | p. 314 |
| Non-stationary Work-Load | |
| Dynamic Bandwidth and Resource Constraints | |
| AUS Filtering Process | |
| Interval Based Dynamic Scheduling | |
| 10.4 Work-Load Characterization | p. 320 |
| 10.5 Dynamic Scheduling at the Server | p. 322 |
| A Multi-criteria Scheduling Problem | |
| Computation Complexity of the Multi-criteria Scheduling Problem | |
| 10.6 The Proposed Neuro-Fuzzy Scheduler | p. 328 |
| Hybrid Learning Algorithm | |
| NFS Heuristics | |
| 10.7 Performance Evaluation | p. 338 |
| The Learned Fuzzy Logic Rules | |
| Learned Membership Functions | |
| Performance Results | |
| 10.8 Conclusion | p. 350 |
| 10.9 Appendix | p. 351 |
| 10.10 References | p. 355 |
| 11 A Neuro-Fuzzy System Based on Logical Interpretation of If-Then RulesJacek Leski and Norbert Henzel | |
| Abstract | p. 360 |
| 11.1 Introduction | p. 360 |
| 11.2 An approach to axiomatic definition of fuzzy implication | p. 362 |
| 11.3 Reasoning using fuzzy implications and generalized modus ponens | p. 369 |
| 11.4 Fundamentals of fuzzy systems | p. 372 |
| 11.5 Fuzzy system with logical interpretation of if-then rules | p. 375 |
| 11.6 Application of ANBLIR to pattern recognition | p. 381 |
| 11.7 Numerical examples | p. 382 |
| Application to forensic glass classification | |
| Application to the famous iris problem | |
| Application to wine recognition data | |
| Application to MONKS problems | |
| 11.8 Conclusions | p. 386 |
| 11.9 References | p. 387 |
